Earticle

초록

영어

In allusion to the lack of a large number of fault data samples in machinery fault diagnosis, the thesis put forward a new method of machinery fault diagnosis based on support vector machine. And the principle and algorithm of the method has been introduced. Then the multi-fault classifier has been built using simulative fault data. This diagnostic method only need a small amount of time-domain fault data samples to train the fault classification, and don’t need to extract the feature amount. In addition, we verify the correctness of this fault classifier through the application of fault classification in steam turbine generator set. The diagnostic method is simple and has strong ability of fault classification.

목차

Abstract
 1. Introduction
 2. Basic principle of support vector machine classification
 3. The Choice of Kernel Function
 4. Fault Diagnosis of Steam Turbine Generator Set
  4.1. The Establishment of Multi-Fault Classifier
  4.2. The Testing Result
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Lin Sang [ Institute of Electrical and Information Engineering, Heilongjiang University of Science and Technology, Harbin, China ]
• Tieshan Zhang [ Institute of Electrical Power and Information Engineering, China University of Mining and Technology Yinchuan College, Yinchuan, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605